Update app.py

app.py

@@ -26,31 +26,30 @@ if st.button("Predict"):
         
         # Extract the predictions
         predictions = outputs.logits.squeeze()
-
+        
         # Convert to numpy array if necessary
         predicted_scores = predictions.numpy()
-
-        # Apply a significant uniform reduction (e.g., reduce by 70%)
-        reduction_factor = 0.3  # Reduce scores by 70%
+        
+        # Apply a significant uniform reduction (e.g., reduce by 80%)
+        reduction_factor = 0.6  # Reduce scores by 80%
         adjusted_scores = predicted_scores * reduction_factor
-
+        
         # Ensure scores do not go below zero
         adjusted_scores = np.maximum(adjusted_scores, 0)
-
+        
         # Normalize the scores to ensure they fall within the 0-9 range
         normalized_scores = (adjusted_scores / adjusted_scores.max()) * 9  # Scale to 9
-
-        # Apply an additional reduction to the overall score if needed
-        additional_reduction = 2.5  # Further reduce overall score
-        overall_score_index = len(normalized_scores) - 1
-        normalized_scores[overall_score_index] = max(normalized_scores[overall_score_index] - additional_reduction, 0)
-
+        
+        # Apply additional reductions to all scores
+        additional_reduction = 1.5  # Further reduce all scores
+        normalized_scores = np.maximum(normalized_scores - additional_reduction, 0)
+        
         # Round the scores
         rounded_scores = np.round(normalized_scores * 2) / 2
-
+        
         # Display the predictions
         labels = ["Task Achievement", "Coherence and Cohesion", "Vocabulary", "Grammar", "Overall"]
         for label, score in zip(labels, rounded_scores):
             st.write(f"{label}: {score:.1f}")
     else:
-        st.write("Please enter some text to get scores.")
+        st.write("Please enter some text to get scores.")